Lab9_jad

library(tidyverse)
library(lubridate)
library(maps)
library(viridis)
library(plotly)
library(dplyr)

#REPORT DATA
report_03_11_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-11-2020.csv")) %>%
  rename(Country_Region = "Country/Region", Province_State = "Province/State")
report_03_23_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-23-2020.csv"))

#TIME SERIES DATA
time_series_confirmed <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")
time_series_confirmed_long <- time_series_confirmed %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long),
               names_to = "Date", values_to = "Confirmed")

time_series_deaths <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")
time_series_deaths_long <- time_series_deaths %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long),
               names_to = "Date", values_to = "Deaths")

time_series_confirmed_long <- time_series_confirmed_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)

time_series_deaths_long <- time_series_deaths_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".") %>% 
  select(Key, Deaths)

time_series_long_joined <- full_join(time_series_confirmed_long,
                                     time_series_deaths_long, by = c("Key"))

#FOR RECOVERED DATA
time_series_recovered <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")

time_series_recovered_long <- time_series_recovered %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long),
               names_to = "Date", values_to = "Recovered")

time_series_recovered_long <- time_series_recovered_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".") %>% 
  select(Key, Recovered)
time_series_long_joined <- full_join(time_series_long_joined,
                                     time_series_recovered_long, by = c("Key")) %>% 
  select(-Key)

time_series_long_joined$Date <- mdy(time_series_long_joined$Date)

time_series_long_joined_counts <- time_series_long_joined %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
               names_to = "Report_Type", values_to = "Counts")

#EXERCISE PART 1
joined_interactive <- time_series_long_joined_counts %>% 
  group_by(Country_Region, Report_Type, Date) %>% 
  summarise(Counts = sum(Counts)) %>% 
  filter (Country_Region %in% c("US", "China", "Italy", "Japan")) %>% 
  filter (Report_Type %in% c("Confirmed", "Deaths", "Recovered")) %>% 
  ggplot(aes(x = Date,  y = log2(Counts), fill = Report_Type)) +
  geom_point() +
  geom_line() +
  ggtitle("Worldwide COVID-19 Cases") +
  facet_grid(Country_Region~.)
ggplotly(joined_interactive)

#EXERCISE PART 2

time_series_casefatality_long <- time_series_long_joined %>% 
  group_by(Country_Region) %>% 
  summarise(Deaths = sum(Deaths), Confimed = sum(Confirmed)) %>%
  filter (Country_Region %in% c("US", "China", "Italy", "Japan")) %>% 
  mutate(Case_Fatality_Rate = Deaths/Confimed) %>% 
  arrange(desc(Case_Fatality_Rate)) %>% 
  ggplot(aes(x = Country_Region,  y = Case_Fatality_Rate)) +
  geom_col()+
  geom_line() +
  ggtitle("Worldwide COVID-19 Case Fatality Rate")

head(time_series_casefatality_long)

## $data
## # A tibble: 4 x 4
##   Country_Region  Deaths Confimed Case_Fatality_Rate
##   <chr>            <dbl>    <dbl>              <dbl>
## 1 Italy           934608  7406494             0.126 
## 2 US             1453268 27613580             0.0526
## 3 China           291906  7190724             0.0406
## 4 Japan             9970   371910             0.0268
## 
## $layers
## $layers[[1]]
## geom_col: width = NULL, na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_stack 
## 
## $layers[[2]]
## geom_line: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## 
## $scales
## <ggproto object: Class ScalesList, gg>
##     add: function
##     clone: function
##     find: function
##     get_scales: function
##     has_scale: function
##     input: function
##     n: function
##     non_position_scales: function
##     scales: list
##     super:  <ggproto object: Class ScalesList, gg>
## 
## $mapping
## Aesthetic mapping: 
## * `x` -> `Country_Region`
## * `y` -> `Case_Fatality_Rate`
## 
## $theme
## list()
## 
## $coordinates
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: TRUE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_params: function
##     setup_params: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>

ggplotly(time_series_casefatality_long)